Statistical and methodological myths and urban legends: doctrine, verity and fable in organizational and social sciences

Table of contents

Part 1. Statistical Issues. Daniel A. Newman, Missing Data Techniques and Low Response Rates: The Role of Systematic Nonresponse Parameters. Michael J. Zickar, Alison A. Broadfoot, The Partial Revival of a Dead Horse? Comparing Classical Test Theory and Item Response Theory. Deborah L. Bandalos, Meggen R. Boehm, Four Common Misconceptions in Exploratory Factor Analysis. Adam W. Meade, Tara S. Behrend, Charles E. Lance, Dr. StrangeLOVE, or: How I Learned to Stop Worrying and Love Omitted Variables. James M. LeBreton, Jane Wu, Mark N. Bing, The Truth(s) on Testing for Mediation in the Social and Organizational Sciences. Jeffrey R. Edwards, Seven Deadly Myths of Testing Moderation in Organizational Research. Robert J. Vandenberg, Darrin M. Grelle, Alternative Model Specifications in Structural Equation Modeling: Facts, Fictions, and Truth. Ronald S. Landis, Bryan D. Edwards, Jose M. Cortina, On the Practice of Allowing Correlated Residuals Among Indicators in Structural Equation Models. Part 2. Methodological Issues. Lillian T. Eby, Carrie S. Hurst, Marcus M. Butts, Qualitative Research: The Red-Headed Stepchild in Organizational and Social Science Research? Scott Highhouse, Jennifer Z. Gillespie, Do Samples Really Matter That Much? Herman Aguinis, Erika E. Harden, Sample Size Rules of Thumb: Evaluating Three Common Practices. Jose M. Cortina, Ronald S. Landis, When Small Effect Sizes Tell a Big Story, and When Large Effect Sizes Don’t. David Chan, Why Ask Me? Are Self-report Data Really that Bad? Charles E. Lance, Lisa E. Baranik, Abby R. Lau, Elizabeth A. Scharlau, If It Ain’t Trait It Must Be Method: (Mis)application of the Multitrait Multimethod Design in Organizational Research. Marcus M. Butts, Thomas W. H. Ng, Chopped Liver? OK. Chopped Data? Not OK .

This book provides an up-to-date review of commonly undertaken methodological and statistical practices that are sustained, in part, upon sound rationale and justification and, in part, upon unfounded lore. Some examples of these "methodological urban legends", as we refer to them in this book, are characterized by manuscript critiques such as: (a) "your self-report measures suffer from common method bias"; (b) "your item-to-subject ratios are too low"; (c) "you can't generalize these findings to the real world"; or (d) "your effect sizes are too low". Historically, there is a kernel of truth to most of these legends, but in many cases that truth has been long forgotten, ignored or embellished beyond recognition. This book examines several such legends. Each chapter is organized to address: (a) what the legend is that "we (almost) all know to be true"; (b) what the "kernel of truth" is to each legend; (c) what the myths are that have developed around this kernel of truth; and (d) what the state of the practice should be. This book meets an important need for the accumulation and integration of these methodological and statistical practices.

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